Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A Method for Constructing Informative Priors for Bayesian Modeling of Occupational Hygiene Data.

Harrison Quick1, Tran Huynh2, Gurumurthy Ramachandran3

  • 1Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA 19104, USA.

Annals of Work Exposures and Health
|April 11, 2017
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Developing PPE management tools for use in a pandemic.

Journal of occupational and environmental hygiene·2026
Same author

Invisible Threats, Concrete Actions: Analyzing and Enhancing OSHA's Infectious Disease Framework.

Health security·2026
Same author

Bayesian Models to Generate Small Area Estimates of Population Health: Tutorial for Using Rate Stabilizing Tools and Their Output.

JMIR public health and surveillance·2026
Same author

Inactivation of airborne pathogen surrogates by triethylene glycol.

Applied and environmental microbiology·2026
Same author

A Markov model for fate and transport of Staphylococcus aureus at a swine barn and proposed interventions to reduce worker exposures.

Annals of work exposures and health·2026
Same author

Associations between airborne crude oil chemicals and neurological symptoms among workers in the gulf long-term follow-up study.

The Science of the total environment·2025

This study introduces a new Bayesian method for occupational hygienists to create robust prior distributions. This approach enhances data analysis accuracy despite resource limitations, improving statistical inference.

Area of Science:

  • Occupational Hygiene
  • Statistical Modeling
  • Bayesian Inference

Background:

  • Limited resources in occupational hygiene necessitate advanced statistical methods.
  • Bayesian methods offer refined inference by incorporating prior information.
  • Risk of bias exists if prior information conflicts with observed data.

Purpose of the Study:

  • To propose a novel method for constructing informative prior distributions.
  • To ensure priors are intuitive to specify and robust to bias for normal and lognormal data.
  • To provide practical guidance for occupational hygienists.

Main Methods:

  • Development of a new methodology for creating informative prior distributions.
  • Application to normal and lognormal data distributions.
Keywords:
decision makinghierarchical modelingprior sample sizesparse datatruncated priors

Related Experiment Videos

  • Step-by-step implementation guide with an illustrative example.

    • Main Results:

    • The proposed method allows for the construction of intuitive and bias-robust informative priors.
    • Demonstrated practical utility through a detailed example.
    • Refined statistical inference achievable even with limited resources.

    Conclusions:

    • The developed method offers a valuable tool for occupational hygienists.
    • Recommendations provided for the general application of these priors.
    • Improved accuracy and precision in occupational hygiene assessments are facilitated.